Traditional cameras use a lens or lenses to image each point in a scene onto a single point on a sensor. In digital cameras, the sensor is a two-dimensional array of picture elements, or “pixels,” that encodes the imaged scene into digital image data for storage, processing, and reproduction.
Digital imaging has enabled new imaging architectures. Cathey and Dowski took an early and conceptually important step away from the traditional model by exploiting digital processing. They designed a cubic-phase optical plate which, when inserted into the optical path of a traditional camera, led to an image whose (significant) blur was independent of the object depth: the image on the sensor plane did not “look good” as it would in a traditional camera. However, subsequent image processing sharpened the entire blurred image, thus leading to enhanced depth of field. Since then the field of computational imaging has explored imaging architectures in which the raw signals do not superficially resemble a traditional image; instead, the final image is computed from such signals. More and more of the total imaging “burden” is borne by computation, thereby expanding the class of usable optical components. In this way, many optical aberrations can be corrected computationally rather than optically. This imaging paradigm has led to new conceptual foundations of joint design of optics and image processing, as well as a wide range of non-standard imaging architectures such as plenoptic, coded-aperture and multi-aperture systems, each with associated methods of signal processing.